Deep learning for improving the spatial resolution of magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast. Approach. Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings. Main results. We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two. Significance. This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

Automated summary

This study proposes a deep-learning approach to improve the spatial resolution of magnetic particle imaging (MPI) by fusing a dual-sampling convolutional neural network (FDS-MPI). The results from simulation and phantom experiments demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two, which could facilitate the future preclinical application of medical imaging modalities.